app.py

import spaces
import gradio as gr
import numpy as np
import random
import generation_sdxl
import functools
from diffusers import DiffusionPipeline, UNet2DConditionModel, StableDiffusionXLPipeline, DDIMScheduler
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

torch.cuda.max_memory_allocated(device=device)
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
pipe = StableDiffusionXLPipeline.from_pretrained(model_id, 
                                               torch_dtype=torch.float16, 
                                               scheduler=DDIMScheduler.from_pretrained(model_id, subfolder="scheduler"),
                                               variant="fp16").to(device)
pipe = pipe.to(device)
unet = UNet2DConditionModel.from_pretrained("dbaranchuk/sdxl-cfg-distill-unet").to(device)
pipe.unet = unet 
pipe.load_lora_weights("dbaranchuk/icd-lora-sdxl",
                         weight_name='reverse-249-499-699-999.safetensors')
pipe.fuse_lora()
pipe.to(dtype=torch.float16, device=device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

@spaces.GPU(duration=30)
def infer(prompt, seed, randomize_seed, tau, 
          guidance_scale):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    prompt = [prompt]
    text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
    tokenizers = [pipe.tokenizer, pipe.tokenizer_2]

    compute_embeddings_fn = functools.partial(
        generation_sdxl.compute_embeddings,
        proportion_empty_prompts=0,
        text_encoders=text_encoders,
        tokenizers=tokenizers,
    )

    if tau < 1.0:
        use_dynamic_guidance=True
    else:
        use_dynamic_guidance=False

    images = generation_sdxl.sample_deterministic(
            pipe,
            prompt,
            num_inference_steps=4,
            generator=generator,
            guidance_scale=guidance_scale,
            is_sdxl=True,
            timesteps=[249, 499, 699, 999],
            use_dynamic_guidance=use_dynamic_guidance,
            tau1=tau,
            tau2=tau,
            compute_embeddings_fn=compute_embeddings_fn
        )[0]

    return images

examples = [
    "An astronaut riding a green horse",
    'Long-exposure night photography of a starry sky over a mountain range, with light trails.',
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "A portrait of a girl with blonde, tousled hair, blue eyes",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            f"""
        # ⚡ Invertible Consistency Distillation ⚡ 
        # ⚡ Image Generation with 4-step iCD-XL ⚡
        This is a demo of [Invertible Consistency Distillation](https://yandex-research.github.io/invertible-cd/), 
        a diffusion distillation method proposed in [Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps](https://arxiv.org/abs/2406.14539)
        by [Yandex Research](https://github.com/yandex-research).
        Currently running on {power_device}.
        """
        )
        gr.Markdown(
            "If you enjoy the space, feel free to give a ⭐ to the <a href='https://github.com/yandex-research/invertible-cd' target='_blank'>Github Repo</a>. [![GitHub Stars](https://img.shields.io/github/stars/yandex-research/invertible-cd?style=social)](https://github.com/yandex-research/invertible-cd)"
        )

        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=19.0,
                    step=1.0,
                    value=7.0,
                )
                
                dynamic_guidance_tau = gr.Slider(
                    label="Dynamic guidance tau",
                    minimum=0,
                    maximum=1,
                    step=0.1,
                    value=1.0,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt],
            cache_examples=False
        )
    run_button.click(
        fn = infer,
        inputs = [prompt, seed, randomize_seed, dynamic_guidance_tau, guidance_scale],
        outputs = [result]
    )

demo.queue().launch(share=False)